Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions.

ABSTRACT: Biological brain age predicted using machine learning models based on high-resolution imaging data has been suggested as a potential biomarker for neurological and cerebrovascular diseases. In this work, we aimed to develop deep learning models to predict the biological brain age using structural magnetic resonance imaging and angiography datasets from a large database of 2074 adults (21-81 years). Since different imaging modalities can provide complementary information, combining them might allow to identify more complex aging patterns, with angiography data, for instance, showing vascular aging effects complementary to the atrophic brain tissue changes seen in T1-weighted MRI sequences. We used saliency maps to investigate the contribution of cortical, subcortical, and arterial structure

SUBMITTER: Mouches P 

PROVIDER: S-EPMC9057090 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature